Algorithms

The Algorithm class is the highest-level API in RLlib responsible for WHEN and WHAT of RL algorithms. Things like WHEN should we sample the algorithm, WHEN should we perform a neural network update, and so on. The HOW will be delegated to components such as RolloutWorker, etc..

It is the main entry point for RLlib users to interact with RLlib’s algorithms.

It allows you to train and evaluate policies, save an experiment’s progress and restore from a prior saved experiment when continuing an RL run. Algorithm is a sub-class of Trainable and thus fully supports distributed hyperparameter tuning for RL.

A typical RLlib Algorithm object: Algorithms are normally comprised of N RolloutWorkers that orchestrated via a EnvRunnerGroup object. Each worker own its own a set of Policy objects and their NN models per worker, plus a BaseEnv instance per worker.

Building Custom Algorithm Classes

Warning

As of Ray >= 1.9, it is no longer recommended to use the build_trainer() utility function for creating custom Algorithm sub-classes. Instead, follow the simple guidelines here for directly sub-classing from Algorithm.

In order to create a custom Algorithm, sub-class the Algorithm class and override one or more of its methods. Those are in particular:

• setup()

• get_default_config()

• get_default_policy_class()

• training_step()

See here for an example on how to override Algorithm.

Algorithm API

Construction and setup

Algorithm	An RLlib algorithm responsible for training one or more neural network models.
setup	Subclasses should override this for custom initialization.
get_default_config
env_runner	The local EnvRunner instance within the algo's EnvRunnerGroup.
eval_env_runner	The local EnvRunner instance within the algo's evaluation EnvRunnerGroup.

Training

train	Runs one logical iteration of training.
training_step	Default single iteration logic of an algorithm.

Saving and restoring

save_to_path	Saves the state of the implementing class (or state) to path.
restore_from_path	Restores the state of the implementing class from the given path.
from_checkpoint	Creates a new algorithm instance from a given checkpoint.
get_state	Returns the implementing class's current state as a dict.
set_state	Sets the implementing class' state to the given state dict.

Evaluation

evaluate

Evaluates current policy under evaluation_config settings.

Multi Agent

get_module	Returns the (single-agent) RLModule with model_id (None if ID not found).
add_policy	Adds a new policy to this Algorithm.
remove_policy	Removes a policy from this Algorithm.